Multi-stream Data Analytics for Enhanced Performance Prediction in Fantasy Football
This addresses the need for more accurate performance predictions in fantasy football, though it is incremental as it builds on existing predictors by adding new data sources.
The paper tackled the problem of predicting fantasy football player performance by incorporating external factors like injuries and social media opinions, resulting in a model that outperformed statistical predictors by over 300 points and ranked in the top 0.5% of players.
Fantasy Premier League (FPL) performance predictors tend to base their algorithms purely on historical statistical data. The main problems with this approach is that external factors such as injuries, managerial decisions and other tournament match statistics can never be factored into the final predictions. In this paper, we present a new method for predicting future player performances by automatically incorporating human feedback into our model. Through statistical data analysis such as previous performances, upcoming fixture difficulty ratings, betting market analysis, opinions of the general-public and experts alike via social media and web articles, we can improve our understanding of who is likely to perform well in upcoming matches. When tested on the English Premier League 2018/19 season, the model outperformed regular statistical predictors by over 300 points, an average of 11 points per week, ranking within the top 0.5% of players rank 30,000 out of over 6.5 million players.